Sparse Structural Similarity for Objective Image Quality Assessment

Xiang Zhang; Shiqi Wang; Ke Gu; Tingting Jiang; Siwei Ma; Wen Gao

doi:10.1109/SMC.2015.276

Sparse Structural Similarity for Objective Image Quality Assessment

Xiang Zhang, Shiqi Wang, Ke Gu, Tingting Jiang, Siwei Ma, Wen Gao

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

15 Citations (Scopus)

Abstract

In this paper, a novel full-reference (FR) image quality assessment (IQA) metric based on sparse representation is proposed. Sparse representation has been widely applied in many applications such as image denoising and restoration. It is a high-efficiency way in representing sparse and redundant natural images. Also it has been shown to be highly related to the human visual perception, which is characterized by a set of responses of neurons in visual cortex. In this paper, the sparse representation is applied in decomposing natural images into multiple layers depending on the visual importance. Inspired by these observations, a novel IQA metric called sparse structural similarity is proposed by measuring the fidelity of the stimulation of visual cortices. Experimental results on public databases indicate that the proposed method is effective in predicting subjective evaluation and as compared to state-of-The-Art FR-IQA methods.

Original language	English
Title of host publication	Proceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015
Publisher	IEEE
Pages	1561-1566
ISBN (Electronic)	978-1-4799-8697-2
DOIs	https://doi.org/10.1109/SMC.2015.276
Publication status	Published - Oct 2015
Externally published	Yes
Event	2015 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2015) - City University of Hong Kong, Hong Kong, China Duration: 9 Oct 2015 → 12 Oct 2015

Conference

Conference	2015 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2015)
Place	Hong Kong, China
Period	9/10/15 → 12/10/15

Research Keywords

Image quality assessment (IQA)
orthogonal matching pursuit (OMP)
sparse representation

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10.1109/SMC.2015.276

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@inproceedings{029b7a1892324046a43da21b41671992,

title = "Sparse Structural Similarity for Objective Image Quality Assessment",

abstract = "In this paper, a novel full-reference (FR) image quality assessment (IQA) metric based on sparse representation is proposed. Sparse representation has been widely applied in many applications such as image denoising and restoration. It is a high-efficiency way in representing sparse and redundant natural images. Also it has been shown to be highly related to the human visual perception, which is characterized by a set of responses of neurons in visual cortex. In this paper, the sparse representation is applied in decomposing natural images into multiple layers depending on the visual importance. Inspired by these observations, a novel IQA metric called sparse structural similarity is proposed by measuring the fidelity of the stimulation of visual cortices. Experimental results on public databases indicate that the proposed method is effective in predicting subjective evaluation and as compared to state-of-The-Art FR-IQA methods.",

keywords = "Image quality assessment (IQA), orthogonal matching pursuit (OMP), sparse representation",

author = "Xiang Zhang and Shiqi Wang and Ke Gu and Tingting Jiang and Siwei Ma and Wen Gao",

year = "2015",

month = oct,

doi = "10.1109/SMC.2015.276",

language = "English",

pages = "1561--1566",

booktitle = "Proceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015",

publisher = "IEEE",

address = "United States",

note = "2015 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2015) ; Conference date: 09-10-2015 Through 12-10-2015",

}

Zhang, X, Wang, S, Gu, K, Jiang, T, Ma, S & Gao, W 2015, Sparse Structural Similarity for Objective Image Quality Assessment. in Proceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015., 7379408, IEEE, pp. 1561-1566, 2015 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2015), Hong Kong, China, 9/10/15. https://doi.org/10.1109/SMC.2015.276

Sparse Structural Similarity for Objective Image Quality Assessment. / Zhang, Xiang; Wang, Shiqi; Gu, Ke et al.
Proceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015. IEEE, 2015. p. 1561-1566 7379408.

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Sparse Structural Similarity for Objective Image Quality Assessment

AU - Zhang, Xiang

AU - Wang, Shiqi

AU - Gu, Ke

AU - Jiang, Tingting

AU - Ma, Siwei

AU - Gao, Wen

PY - 2015/10

Y1 - 2015/10

N2 - In this paper, a novel full-reference (FR) image quality assessment (IQA) metric based on sparse representation is proposed. Sparse representation has been widely applied in many applications such as image denoising and restoration. It is a high-efficiency way in representing sparse and redundant natural images. Also it has been shown to be highly related to the human visual perception, which is characterized by a set of responses of neurons in visual cortex. In this paper, the sparse representation is applied in decomposing natural images into multiple layers depending on the visual importance. Inspired by these observations, a novel IQA metric called sparse structural similarity is proposed by measuring the fidelity of the stimulation of visual cortices. Experimental results on public databases indicate that the proposed method is effective in predicting subjective evaluation and as compared to state-of-The-Art FR-IQA methods.

AB - In this paper, a novel full-reference (FR) image quality assessment (IQA) metric based on sparse representation is proposed. Sparse representation has been widely applied in many applications such as image denoising and restoration. It is a high-efficiency way in representing sparse and redundant natural images. Also it has been shown to be highly related to the human visual perception, which is characterized by a set of responses of neurons in visual cortex. In this paper, the sparse representation is applied in decomposing natural images into multiple layers depending on the visual importance. Inspired by these observations, a novel IQA metric called sparse structural similarity is proposed by measuring the fidelity of the stimulation of visual cortices. Experimental results on public databases indicate that the proposed method is effective in predicting subjective evaluation and as compared to state-of-The-Art FR-IQA methods.

KW - Image quality assessment (IQA)

KW - orthogonal matching pursuit (OMP)

KW - sparse representation

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DO - 10.1109/SMC.2015.276

M3 - RGC 32 - Refereed conference paper (with host publication)

SP - 1561

EP - 1566

BT - Proceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015

PB - IEEE

T2 - 2015 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2015)

Y2 - 9 October 2015 through 12 October 2015

ER -

Sparse Structural Similarity for Objective Image Quality Assessment

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